1,721,129 research outputs found
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
Adaptable maps for visual camera localization under dynamic illumination
No full textLocalization within a map, i.e., a model of the environment, is an important ability of autonomous mobile robots. Among the most popular localization techniques are such based on vision due to the low cost and broad availability of color cameras and the richness of information color images provide. However, the appearance of a scene depends on the current illumination and can change dramatically if the lighting varies. That poses a crucial problem to vision-based localization since a previously built map would no longer represent the current state of the environment. In this dissertation, we propose to tackle the issue by explicitly modeling the illumination changes in a physically plausible way. To that end, we develop real-time methods for adapting the map to the current scene lighting and demonstrate that this significantly improves visual camera localization under dynamic illumination.In this thesis, we make three contributions. The first contribution is an approach for creating a 3D reflectance map of an indoor scene from RGB-D images. The reflectance is an illumination-invariant material property that determines how much incident light is reflected by a surface point. In a first step, we measure the radiosity at each scene point, that is, the amount of light projected into the scene. That is achieved by varying the camera's exposure time and fusing the resulting color images. We then deploy the radiosity method, a technique for simulating light transport, to compute the incident light at each point. The reflectance values are calculated as the fraction between the incident light and radiosity. Finally, since inaccuracies in geometry and radiosity reconstruction cause artifacts in reflectance, we further propose a post-processing procedure for reflectance map refinement.The second contribution is a method for adapting the map to dynamic lighting in indoor locations. Specifically, we consider illumination variations constituted by switching on or off artificial light sources, e.g., lamps or ceiling lights. The approach builds upon the previously created radiosity and reflectance map. Radiosity helps to detect and segment switched-on light emitters based on their brightness. For each such light source, we pre-compute its contribution to scene illumination using the reflectance and the radiosity method. By adding up the individual light contributions, we can predict the scene appearance for any configuration of light emitter states. This process is efficiently parallelized on a GPU, enabling real-time performance. Further, we propose a method for detecting the current light setting from a single RGB image, allowing us to adapt the map to the current scene illumination in every frame. Our experiments on real-world data show that the precision and robustness of camera localization are improved under dynamic illumination if the adaptable map is deployed.The third contribution is the extension of the above method to indoor areas illuminated by outdoor lighting, coming in through windows. Our model for outdoor illumination consists of light components contributed by the sun, the sky, and the ground. The sky and the ground lighting are pre-computed offline, using a normalized brightness. However, as the sun position changes throughout the day, the corresponding illumination component is continuously updated in the background, parallel to camera localization. In each frame, we estimate the brightness of the three illumination components according to the current RGB image and make corresponding map adjustments. That allows us to align the map illumination to rapid lighting variations produced by changing weather. Similarly to indoor lighting, we demonstrate that localization robustness is considerably increased under dynamic outdoor illumination. To this end, we perform the localization experiments on data recorded at different times of the day and under different weather conditions
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